repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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Tim-TSENet | Tim-TSENet-main/TSDNET/test_tasnet_one_hot_reg.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model import TSDNet,TSDNet_one_hot,TSDNet_plus_one_hot
from logger.set_logger import setup_logger
import logging
from config.option import parse
import torchaudio
from... | 11,496 | 45.358871 | 170 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/dualrnn_test.py | import os
import torch
from data_loader.AudioReader import AudioReader, write_wav
import argparse
from torch.nn.parallel import data_parallel
from model.model_rnn import Dual_RNN_model
from logger.set_logger import setup_logger
import logging
from config.option import parse
import tqdm
class Separation():
def __i... | 3,384 | 40.790123 | 105 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/trainer/trainer_Tasnet_tse.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from logger.set_logger import setup_logger
from model.loss import get_loss
import torch
import os
import matplotlib.pyplot as plt
from torch.nn.parallel import data_parallel
class Trainer(object):
def __init__(sel... | 13,121 | 44.404844 | 146 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/trainer/trainer_Tasnet_one_hot_regresion.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from logger.set_logger import setup_logger
from model.loss import get_loss, get_loss_one_hot, get_loss_one_hot_focal, get_loss_one_hot_focal_sim,get_loss_one_hot_reg,get_loss_one_hot_reg_two
import torch
import os
impo... | 14,648 | 47.506623 | 147 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/trainer/trainer_Dual_RNN.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from logger.set_logger import setup_logger
from model.loss import Loss
import torch
import os
import matplotlib.pyplot as plt
from torch.nn.parallel import data_parallel
class Trainer(object):
def __init__(self,... | 8,747 | 39.5 | 112 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/trainer/trainer_Tasnet.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from logger.set_logger import setup_logger
from model.loss import get_loss
import torch
import os
import matplotlib.pyplot as plt
from torch.nn.parallel import data_parallel
class Trainer(object):
def __init__(sel... | 12,954 | 44.297203 | 146 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/trainer/trainer_Tasnet_one_hot.py | import sys
sys.path.append('../')
from utils.util import check_parameters
import time
import logging
from logger.set_logger import setup_logger
from model.loss import get_loss, get_loss_one_hot, get_loss_one_hot_focal, get_loss_one_hot_focal_sim
import torch
import os
import matplotlib.pyplot as plt
from torch.nn.para... | 15,168 | 48.734426 | 146 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/data_loader/AudioData.py | import torch.nn.functional as F
from utils import util
import torch
import torchaudio
import sys
sys.path.append('../')
def read_wav(fname, return_rate=False):
'''
Read wavfile using Pytorch audio
input:
fname: wav file path
return_rate: Whether to return the sampli... | 2,751 | 30.272727 | 87 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/data_loader/Dataset.py | import sys
sys.path.append('../')
from data_loader.AudioData import AudioReader
import torch
from torch.utils.data import Dataset
import numpy as np
class Datasets(Dataset):
'''
Load audio data
mix_scp: file path of mix audio (type: str)
ref_scp: file path of ground truth audio (type: list[... | 1,666 | 36.886364 | 183 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/data_loader/AudioReader.py | import sys
sys.path.append('../')
import torchaudio
import torch
from utils.util import handle_scp
def read_wav(fname, return_rate=False):
'''
Read wavfile using Pytorch audio
input:
fname: wav file path
return_rate: Whether to return the sampling rate
outp... | 2,556 | 28.732558 | 82 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/data_loader/Dataset_light.py | import sys
sys.path.append('../')
import torch
from torch.utils.data import DataLoader, Dataset
import torch.nn.functional as F
import random
import numpy as np
import soundfile as sf
import torchaudio
from utils.util import handle_scp, handle_scp_inf
from model.model import STFT
import os
import pickle
import math
nFr... | 9,413 | 37.740741 | 191 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/utils/util.py | import torch
import torch.nn as nn
def handle_scp(scp_path):
'''
Read scp file script
input:
scp_path: .scp file's file path
output:
scp_dict: {'key':'wave file path'}
'''
scp_dict = dict()
line = 0
lines = open(scp_path, 'r').readlines()
for l in lines:
... | 1,932 | 26.225352 | 79 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/model_rnn.py | import sys
sys.path.append('../')
import torch.nn.functional as F
from torch import nn
import torch
from utils.util import check_parameters
import warnings
warnings.filterwarnings('ignore')
class GlobalLayerNorm(nn.Module):
'''
Calculate Global Layer Normalization
dim: (int or list or torch.Size) ... | 15,142 | 35.314149 | 131 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/PANNS.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(l... | 5,456 | 39.422222 | 107 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/loss.py | import torch
from itertools import permutations
import numpy as np
from pypesq import pesq
import torch.nn as nn
class FocalLoss(nn.Module):
def __init__(self,alpha=0.35, gamma=2):
super(FocalLoss,self).__init__()
self.gamma = gamma
self.alpha = alpha
self.eps = 1e-9
#self.bc... | 7,028 | 29.428571 | 121 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/model.py | import torch
from torch import nn
import torch.nn.functional as F
import sys
sys.path.append('../')
from utils.util import check_parameters
from model.PANNS import CNN10
from model.tsd import TSD, TSD2, TSD2_tse, TSD_plus, TSD_plus_sim, TSD_IS,TSD_regresion,TSD_regresion_two_cls
import math
def init_kernel(frame_len,
... | 31,331 | 38.067332 | 177 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/model_tf.py | import torch
from torch import nn
import torch.nn.functional as F
import sys
import pickle
sys.path.append('../')
from utils.util import check_parameters
from model.PANNS import ResNet38, CNN10
from model.tsd import TSD
def init_kernel(frame_len,
frame_hop,
num_fft=None,
... | 17,741 | 34.342629 | 129 | py |
Tim-TSENet | Tim-TSENet-main/TSDNET/model/tsd.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torchlibrosa.stft import Spectrogram, LogmelFilterBank
from torchlibrosa.augmentation import SpecAugmentation
def init_layer(layer):
"""Initialize a Linear or Convolutional layer. """
nn.init.xavier_uniform_(layer.weight)
if hasattr(lay... | 37,974 | 41.100887 | 109 | py |
SOF-VSR | SOF-VSR-master/TIP/modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torch.autograd import Variable
class SOFVSR(nn.Module):
def __init__(self, cfg, n_frames=3, is_training=True):
super(SOFVSR, self).__init__()
self.scale = cfg.scale
self.is_training = is_training
... | 8,356 | 37.334862 | 132 | py |
SOF-VSR | SOF-VSR-master/TIP/data_utils.py | from PIL import Image
from torch.utils.data.dataset import Dataset
from modules import optical_flow_warp
import numpy as np
import os
import torch
import random
class TrainsetLoader(Dataset):
def __init__(self, cfg):
super(TrainsetLoader).__init__()
self.trainset_dir = cfg.trainset_dir
self... | 7,589 | 36.389163 | 143 | py |
SOF-VSR | SOF-VSR-master/TIP/train.py | from torch.autograd import Variable
from torch.utils.data import DataLoader
from modules import SOFVSR
from data_utils import TrainsetLoader, OFR_loss
import torch.backends.cudnn as cudnn
import argparse
import torch
import numpy as np
import torch.nn.functional as F
import os
def parse_args():
parser = argparse.... | 3,333 | 31.686275 | 100 | py |
SOF-VSR | SOF-VSR-master/TIP/demo_Vid4.py | from torch.autograd import Variable
from torch.utils.data import DataLoader
from data_utils import TestsetLoader, ycbcr2rgb
from modules import SOFVSR
from torchvision.transforms import ToPILImage
import numpy as np
import os
import argparse
import torch
def parse_args():
parser = argparse.ArgumentParser()
pa... | 4,835 | 39.3 | 154 | py |
SOF-VSR | SOF-VSR-master/ACCV/modules.py | import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
def optical_flow_warp(image, image_optical_flow):
"""
Arguments
image_ref: reference images tensor, (b, c, h, w)
image_optical_flow: optical ... | 8,788 | 46.766304 | 169 | py |
SOF-VSR | SOF-VSR-master/ACCV/data_utils.py | import numpy as np
from PIL import Image
import os
import torch
from torch.utils.data.dataset import Dataset
import math
import random
class TrainsetLoader(Dataset):
def __init__(self, trainset_dir, upscale_factor, patch_size, n_iters):
super(TrainsetLoader).__init__()
self.trainset_dir = trainset_... | 6,999 | 40.666667 | 143 | py |
SOF-VSR | SOF-VSR-master/ACCV/train.py | import os
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.backends.cudnn as cudnn
from modules import SOFVSR, optical_flow_warp
import argparse
from data_utils import TrainsetLoader
import numpy as np
import matplotlib.pyplot as plt
def parse_args():
parser = ... | 3,641 | 38.16129 | 129 | py |
SOF-VSR | SOF-VSR-master/ACCV/demo_Vid4.py | import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
from data_utils import TestsetLoader, ycbcr2rgb
import numpy as np
from torchvision.transforms import ToPILImage
import os
import argparse
from modules import SOFVSR
import math
def parse_args():
parser = argparse.ArgumentPars... | 3,762 | 37.010101 | 106 | py |
SOF-VSR | SOF-VSR-master/ACCV/metrics/evaluation.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
from math import log10
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
return gauss / gauss.s... | 2,423 | 40.793103 | 114 | py |
finmag | finmag-master/doc/conf.py | # -*- coding: utf-8 -*-
#
# Finmag documentation build configuration file, created by
# sphinx-quickstart on Thu Feb 23 12:34:28 2012.
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All ... | 8,297 | 31.541176 | 80 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/test.py | ########################################################
# This is an example of the training and test procedure
# You need to adjust the training and test dataloader based on your data
# CopyRight @ Xuesong Niu
########################################################
import torch
import torch.nn as nn
import torch.nn... | 6,206 | 39.835526 | 159 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/database/Pixelmap.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import os
import shutil
import numpy as np
from torch.utils.data import Dataset, DataLoader
import scipy.io as sio
from PIL import Image
import torchvision.transforms.functional as transF
... | 2,154 | 30.231884 | 106 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/loss/loss_cross.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable, Function
import os
import shutil
import numpy as np
import scipy.io as sio
from scipy.stats import norm
class Cross_loss(nn.Module):
def __init__(self, lambda_cross_fhr = 0.000005, la... | 1,533 | 36.414634 | 151 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/loss/loss_SNR.py | import math
import torch
from torch.autograd import Variable
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
class SNR_loss(nn.Module):
def __init__(self, clip_length = 300, delta = 3, loss_type = 1, use_wave = False):
super(SNR_loss, self).__init__()
self.clip_length = cl... | 3,482 | 31.858491 | 130 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/loss/loss_r.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable, Function
import os
import shutil
import numpy as np
import scipy.io as sio
from scipy.stats import norm
class Neg_Pearson(nn.Module): # Pearson range [-1, 1] so if < 0, abs|loss| ; if >0... | 1,249 | 29.487805 | 110 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/model/resnet.py | import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from torch.autograd import Variable
import torch
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
're... | 16,942 | 33.577551 | 87 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/model/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.autograd import Variable
import os, sys
import shutil
import numpy as np
import scipy.io as sio
sys.path.append('..');
from utils.model.resnet import resnet18, resnet_small;
from utils.model.resnet_stconv import ... | 353 | 16.7 | 54 | py |
CVD-Physiological-Measurement | CVD-Physiological-Measurement-master/utils/model/model_disentangle.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import os, sys
import shutil
import numpy as np
import scipy.io as sio
sys.path.append('..');
from utils.model.resnet import resnet18, resnet18_part;
import time
class ResidualBlock(nn.Module):
"""Residual Block."""
def __init__(self, dim_in,... | 5,936 | 34.76506 | 136 | py |
CREPE | CREPE-master/crepe_prod_eval_cyclip.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ast
import argparse
import logging
import os
from PIL import Image, ImageFile
from dataclasses import da... | 5,815 | 32.045455 | 135 | py |
CREPE | CREPE-master/crepe_prod_eval_albef.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
from PIL import Image
from time import time
import torch
from torch import nn
from to... | 7,966 | 34.887387 | 135 | py |
CREPE | CREPE-master/crepe_prod_eval_flava.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
import ast
import logging
import os
from PIL import Image
from dataclasses import dataclass
from time import t... | 6,056 | 31.918478 | 135 | py |
CREPE | CREPE-master/crepe_prod_eval_clip.py | import logging
import os
from time import time
import json
import torch
import torchvision.transforms.functional as TF
import clip
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
from crepe_eval_utils import BaseCsvDataset, get_one2many_rank, get_one2many_metrics, DataInfo
from crepe_p... | 5,220 | 30.642424 | 135 | py |
CREPE | CREPE-master/crepe_compo_eval_open_clip.py | import os
import json
import logging
import torch
import numpy as np
import torch.nn.functional as F
import torchvision.transforms.functional as TF
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from dataclasses import dataclass
from open_clip import tokenize, create... | 7,732 | 34.15 | 160 | py |
CREPE | CREPE-master/crepe_eval_utils.py | import ast
import logging
import os
from PIL import Image
from dataclasses import dataclass
import torch
from torch.utils.data import DataLoader, Dataset
import numpy as np
import pandas as pd
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger()
### DATASET CONSTRUCTION
class BaseCsvDataset(Dataset... | 3,542 | 35.525773 | 146 | py |
CREPE | CREPE-master/open_clip/openai.py | """ OpenAI pretrained model functions
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
import os
import warnings
from typing import Union, List
import torch
from .model import build_model_from_openai_state_dict
from .pretrained import get_pretrained_url, list_pretr... | 4,503 | 34.464567 | 117 | py |
CREPE | CREPE-master/open_clip/transform.py | from torchvision.transforms import Normalize, Compose, RandomResizedCrop, ToTensor, Resize, \
CenterCrop
from PIL import Image
def _convert_to_rgb(image):
return image.convert('RGB')
def image_transform(
image_size: int,
is_train: bool,
mean=(0.48145466, 0.4578275, 0.40821073),
... | 850 | 26.451613 | 93 | py |
CREPE | CREPE-master/open_clip/loss.py | import torch
import torch.distributed.nn
from torch import distributed as dist, nn as nn
from torch.nn import functional as F
try:
import horovod.torch as hvd
except ImportError:
hvd = None
def gather_features(
image_features,
text_features,
local_loss=False,
gather_with_grad=... | 4,658 | 39.513043 | 101 | py |
CREPE | CREPE-master/open_clip/utils.py | from torch import nn as nn
from torchvision.ops.misc import FrozenBatchNorm2d
def freeze_batch_norm_2d(module, module_match={}, name=''):
"""
Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is
itself an instance of either `BatchNorm2d` or `Syn... | 1,850 | 44.146341 | 131 | py |
CREPE | CREPE-master/open_clip/model.py | """ CLIP Model
Adapted from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
from collections import OrderedDict
from dataclasses import dataclass
from typing import Tuple, Union, Callable, Optional
import numpy as np
import torch
import torch.nn.functional as F
from torch impor... | 21,811 | 37.95 | 120 | py |
CREPE | CREPE-master/open_clip/factory.py | import json
import logging
import os
import pathlib
import re
from copy import deepcopy
from pathlib import Path
import torch
from .model import CLIP, convert_weights_to_fp16
from .openai import load_openai_model
from .pretrained import get_pretrained_url, download_pretrained
from .transform import image_transform
... | 5,455 | 34.660131 | 106 | py |
CREPE | CREPE-master/open_clip/tokenizer.py | """ CLIP tokenizer
Copied from https://github.com/openai/CLIP. Originally MIT License, Copyright (c) 2021 OpenAI.
"""
import gzip
import html
import os
from functools import lru_cache
from typing import Union, List
import ftfy
import regex as re
import torch
@lru_cache()
def default_bpe():
return os.path.join(o... | 6,637 | 33.936842 | 121 | py |
CREPE | CREPE-master/open_clip/timm_model.py | """ timm model adapter
Wraps timm (https://github.com/rwightman/pytorch-image-models) models for use as a vision tower in CLIP model.
"""
from collections import OrderedDict
import torch.nn as nn
try:
import timm
from timm.models.layers import Mlp, to_2tuple
from timm.models.layers.attention_pool2d impor... | 4,300 | 39.196262 | 119 | py |
DNN_Rover | DNN_Rover-master/rover/Data.py | import time
import numpy as np
import h5py
import progressbar
import datetime
import tflearn
from tflearn.layers.core import input_data
import torchvision.models as models
from NetworkSwitch import *
import torch
import torch.nn as nn
from scipy.misc import imresize
from skimage.transform import resize
class Data():
... | 3,962 | 35.027273 | 80 | py |
Rep-Learning | Rep-Learning-main/nips_supp/mom_iota.py | from scipy.io import loadmat
import numpy as np
# import torch
# import torch.nn as nn
# import torch.optim as optim
# from torchvision import models
# import torch.utils.data
# from torch.utils import data
# from torch.utils.data import DataLoader, TensorDataset
import scipy
from scipy.optimize import minimize
from sk... | 1,116 | 23.822222 | 66 | py |
Rep-Learning | Rep-Learning-main/nips_supp/SVM_MNIST.py | import numpy as np
import numpy.linalg as npl
import matplotlib.pyplot as plt
from keras.datasets import mnist
import cvxpy as cp
(train_X, train_y), (test_X, test_y) = mnist.load_data()
train_X=train_X.astype(float)
test_X=test_X.astype(float)
for i in range(len(train_X)):
train_X[i]-=np.mean(train_X[i])
trai... | 3,339 | 28.298246 | 115 | py |
FORK | FORK-master/TD3-FORK/TD3_FORK.py | import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 25... | 10,157 | 31.14557 | 108 | py |
FORK | FORK-master/TD3-FORK/utils.py | import numpy as np
import torch
import math
class ReplayBuffer(object):
def __init__(self, state_dim, action_dim, max_size=int(1e6)):
self.max_size = max_size
self.ptr = 0
self.size = 0
self.state = np.zeros((max_size, state_dim))
self.action = np.zeros((max_size, action_dim))
self.next_state = np.zeros... | 3,165 | 32.680851 | 113 | py |
FORK | FORK-master/TD3-FORK/TD3.py | import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3)
# Paper: https://arxiv.org/abs/1802.09477
class Actor(nn.Module):
def _... | 4,752 | 26.473988 | 93 | py |
FORK | FORK-master/TD3-FORK/main_td3_fork.py | import numpy as np
import torch
import gym
import argparse
import os
import copy
import utils
import TD3
import pandas as pd
import json,os
import TD3_FORK
def eval_policy(policy, env_name,eval_episodes=10):
eval_env = gym.make(env_name)
avg_reward = 0.
for _ in range(eval_episodes):
state, done = eval_env.reset(... | 9,548 | 44.255924 | 258 | py |
FORK | FORK-master/SAC-FORK/SAC.py | import os
import torch
import torch.nn.functional as F
from torch.optim import Adam
from utils import soft_update, hard_update
from model import GaussianPolicy, QNetwork, DeterministicPolicy
class SAC(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gamma
self.tau = ... | 5,765 | 45.128 | 133 | py |
FORK | FORK-master/SAC-FORK/utils.py | import numpy as np
import torch
import math
class ReplayBuffer(object):
def __init__(self, state_dim, action_dim, max_size=int(1e6)):
self.max_size = max_size
self.ptr = 0
self.size = 0
self.state = np.zeros((max_size, state_dim))
self.action = np.zeros((max_size, action_dim))
self.next_state = np.zeros... | 3,165 | 32.680851 | 113 | py |
FORK | FORK-master/SAC-FORK/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal
LOG_SIG_MAX = 2
LOG_SIG_MIN = -20
epsilon = 1e-6
# Initialize Policy weights
def weights_init_(m):
if isinstance(m, nn.Linear):
torch.nn.init.xavier_uniform_(m.weight, gain=1)
torch.nn.init.co... | 6,142 | 31.162304 | 84 | py |
FORK | FORK-master/SAC-FORK/main_sac_fork.py | import argparse
import datetime
import gym
import numpy as np
import itertools
import os
import json
import pandas as pd
import torch
import SAC
import SAC_FORK
from replay_memory import ReplayMemory
def eval_policy(policy, env_name, eval_episodes=10):
eval_env = gym.make(env_name)
avg_reward = 0.
for _ ... | 9,260 | 44.62069 | 265 | py |
FORK | FORK-master/SAC-FORK/SAC_FORK.py | import os
import torch
import torch.nn.functional as F
from torch.optim import Adam
from utils import soft_update, hard_update
from model import GaussianPolicy, QNetwork, DeterministicPolicy, Sys_R, SysModel
class SAC_FORK(object):
def __init__(self, num_inputs, action_space, args):
self.gamma = args.gam... | 8,966 | 47.733696 | 172 | py |
Higgs-ML | Higgs-ML-master/cnn.py | from __future__ import print_function
import os, sys
import math
import pandas as pd
import numpy as np
import keras
from keras.models import load_model
from keras import backend as K
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.model_selection import train_test_split
from sklearn import ... | 3,700 | 27.689922 | 155 | py |
Higgs-ML | Higgs-ML-master/func/figure.py | from __future__ import print_function
import keras
import numpy as np
import matplotlib.pyplot as plt
import math
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self,log={}):
self.losses = {'batch':[], 'epoch':[]}
self.accuracy = {'batch':[], 'epoch':[]}
self.val_loss = {'batch':[], 'epoch':[]... | 3,107 | 32.782609 | 158 | py |
Higgs-ML | Higgs-ML-master/func/models.py | import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Activation, Flatten
from keras.callbacks import ModelCheckpoint, EarlyStopping
from keras.optimizers import SGD, Adam, Nadam
def our_model(img_rows,img_cols):
model=Sequential()
model.add(Conv2D(64,(3,3),... | 1,906 | 36.392157 | 107 | py |
hyperas | hyperas-master/setup.py | from setuptools import setup
from setuptools import find_packages
setup(name='hyperas',
version='0.4.1',
description='Simple wrapper for hyperopt to do convenient hyperparameter optimization for Keras models',
url='http://github.com/maxpumperla/hyperas',
download_url='https://github.com/maxpump... | 600 | 39.066667 | 110 | py |
hyperas | hyperas-master/hyperas/optim.py | import inspect
import os
import re
import sys
import nbformat
import numpy as np
from hyperopt import fmin
from nbconvert import PythonExporter
from .ensemble import VotingModel
from .utils import (
remove_imports, remove_all_comments, extract_imports, temp_string,
write_temp_files, determine_indent, with_lin... | 11,352 | 36.468647 | 136 | py |
hyperas | hyperas-master/hyperas/ensemble.py | import numpy as np
from keras.models import model_from_yaml
class VotingModel(object):
def __init__(self, model_list, voting='hard',
weights=None, nb_classes=None):
"""(Weighted) majority vote model for a given list of Keras models.
Parameters
----------
model_li... | 2,567 | 39.125 | 101 | py |
hyperas | hyperas-master/examples/mnist_readme.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from keras.datasets import mnist
from keras.layers.core import Dense, Dropout, Activation
from keras.models import Sequential
from keras.utils import np_utils
from hyperas import optim
from hyperas.distributions import choice, uniform
... | 3,140 | 34.693182 | 83 | py |
hyperas | hyperas-master/examples/lstm.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from keras.preprocessing import sequence
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activat... | 2,374 | 34.447761 | 80 | py |
hyperas | hyperas-master/examples/mnist_distributed.py | from hyperas import optim
from hyperas.distributions import quniform, uniform
from hyperopt import STATUS_OK, tpe, mongoexp
import keras
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.optimizers import RMSprop
from keras.datasets import mnist
import tempfile
def data():
(x_... | 2,561 | 35.084507 | 109 | py |
hyperas | hyperas-master/examples/simple.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.datasets import mni... | 2,737 | 33.225 | 87 | py |
hyperas | hyperas-master/examples/complex.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.datasets import mnist
from keras.utils import np_utils
d... | 3,080 | 35.678571 | 83 | py |
hyperas | hyperas-master/examples/mnist_ensemble.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, rand
from hyperas import optim
from hyperas.distributions import choice, uniform
from sklearn.metrics import accuracy_score
from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layer... | 2,430 | 33.239437 | 87 | py |
hyperas | hyperas-master/examples/use_intermediate_functions.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.datasets import mni... | 3,226 | 32.968421 | 87 | py |
hyperas | hyperas-master/examples/hyperas_in_intermediate_fns.py | import numpy
import random
from keras.datasets import mnist
from keras.models import Model
from keras.layers import Input, Flatten, Dense, Dropout, Lambda
from keras.optimizers import RMSprop
from keras import backend as K
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions... | 4,820 | 35.801527 | 149 | py |
hyperas | hyperas-master/examples/cnn_lstm.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, rand
from hyperas import optim
from hyperas.distributions import uniform, choice
import numpy as np
from keras.preprocessing import sequence
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers.core import Den... | 2,541 | 35.84058 | 84 | py |
hyperas | hyperas-master/examples/cifar_generator_cnn.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import uniform
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
f... | 4,262 | 36.394737 | 94 | py |
hyperas | hyperas-master/tests/test_functional_api.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice
from keras.models import Model
from keras.layers import Dense, Input
from keras.optimizers import RMSprop
from keras.datasets import mnist
from keras.utils import np_uti... | 3,058 | 35.416667 | 90 | py |
hyperas | hyperas-master/tests/test_lr_plateau.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice
from keras.models import Sequential
from keras.layers import Dense, Activation
from keras.datasets import mnist
from keras.utils import np_utils
from keras.callbacks imp... | 2,006 | 34.839286 | 77 | py |
hyperas | hyperas-master/tests/test_e2e.py | from __future__ import print_function
from hyperopt import Trials, STATUS_OK, tpe
from hyperas import optim
from hyperas.distributions import choice, uniform
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation
from keras.optimizers import RMSprop
from keras.datasets import mni... | 3,714 | 31.587719 | 87 | py |
hyperas | hyperas-master/tests/test_optim.py | from keras.datasets import mnist
from keras.utils import np_utils
from hyperas.optim import retrieve_data_string
def test_data():
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype('float32')
... | 1,049 | 31.8125 | 82 | py |
autoagora-agents | autoagora-agents-master/tests/autoagora_agents/test_algorithm.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
import numpy as np
import pytest
import torch
from autoagora_agents import algorithm
from ..fixture import *
def test_predetermined(predeterminedconfig):
agent = algorithm.algorithmgroupfactory(**predeterminedconfig)[0]
obs = np.zer... | 3,131 | 36.285714 | 116 | py |
autoagora-agents | autoagora-agents-master/tests/autoagora_agents/test_distribution.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
import torch
from autoagora_agents import distribution
from ..fixture import *
def test_gaussiandistribution_reset(gaussianconfig):
dist = distribution.distributionfactory(**gaussianconfig)
v = dist.mean # type: ignore
dist._me... | 3,506 | 39.310345 | 88 | py |
autoagora-agents | autoagora-agents-master/tests/autoagora_agents/test_buffer.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
import torch
from autoagora_agents import buffer
def test_buffer():
maxlen = 10
b = buffer.buffer(maxlength=maxlen)
sample = {
"reward": torch.as_tensor([1, 2, 3]),
"action": torch.as_tensor([3, 2, 1]),
}
... | 549 | 19.37037 | 45 | py |
autoagora-agents | autoagora-agents-master/autoagora_agents/algorithm.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
from abc import ABC, abstractmethod
import numpy as np
import torch
from torch import optim
import experiment
from autoagora_agents import buffer
from autoagora_agents.distribution import distributionfactory
class Algorithm(ABC):
"""Bas... | 15,179 | 31.229299 | 93 | py |
autoagora-agents | autoagora-agents-master/autoagora_agents/distribution.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
from abc import ABC, abstractmethod, abstractproperty
from typing import Union
import numpy as np
import torch
from torch import nn
import experiment
ArrayLike = Union[np.ndarray, torch.Tensor]
class Distribution(ABC):
"""The base clas... | 11,376 | 31.229462 | 88 | py |
autoagora-agents | autoagora-agents-master/autoagora_agents/buffer.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
from collections import deque
from typing import Any
import torch
def buffer(*, maxlength: int) -> deque[dict[str, Any]]:
"""Create a buffer.
Keyword Arguments:
maxlength (int): The maximum length of the buffer.
Returns... | 909 | 22.333333 | 58 | py |
autoagora-agents | autoagora-agents-master/autoagora_agents/controller.py | # Copyright 2022-, Semiotic AI, Inc.
# SPDX-License-Identifier: Apache-2.0
import random
from typing import Any
import numpy as np
import torch
from autoagora_agents.algorithm import Algorithm, algorithmgroupfactory
class Controller:
"""Holds all algorithms and routes information to each.
Keyword Argument... | 2,147 | 28.833333 | 87 | py |
flink | flink-master/flink-python/docs/conf.py | ################################################################################
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this... | 7,437 | 32.205357 | 80 | py |
flink | flink-master/flink-python/pyflink/datastream/connectors/cassandra.py | ################################################################################
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this... | 14,269 | 37.567568 | 100 | py |
Robust-Training-for-Time-Series | Robust-Training-for-Time-Series-main/CNNmodel.py | import sys
import os
import numpy as np
import tensorflow as tf
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
import pickle as pkl
from GAK import tf_gak
def clip_tensor(X, eps, norm=np.inf):
if norm not ... | 12,895 | 42.275168 | 140 | py |
LightDepth | LightDepth-main/torch_implementation/scripts/dataloaders.py | # This file is mostly taken from BTS; author: Jin Han Lee, with only slight modifications
import os
import random
import numpy as np
import torch
import torch.utils.data.distributed
from torch.nn import MaxPool2d
from PIL import Image
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms... | 9,302 | 36.063745 | 109 | py |
LightDepth | LightDepth-main/torch_implementation/scripts/models/ordinary_unet.py | import torch
class OrdinaryUNet():
def __init__(self,config):
def UpConv2D(tensor, filters, name, concat_with):
up_i = torch.nn.Upsample((2, 2),mode='bilinear')(tensor)
up_i = torch.cat([up_i, encoder.get_layer(concat_with).output]) # Skip connection
up_i = Conv2D(filter... | 625 | 51.166667 | 110 | py |
LightDepth | LightDepth-main/tf_implementation/scripts/dataloaders.py | import numpy as np
import tensorflow as tf
import tensorflow_addons as tfa
from tensorflow.keras.layers import MaxPooling2D
import os
class OrdinaryDataloader(object):
def __init__(self, config,is_training=True,debug=False):
self.do_flip = config.do_flip
self.do_augment = config.do_augment
... | 8,542 | 39.488152 | 107 | py |
LightDepth | LightDepth-main/tf_implementation/scripts/models/efficient_unet.py | from tensorflow.keras.layers import Layer, InputSpec
import tensorflow as tf
from tensorflow.keras import applications
from tensorflow.keras.layers import Conv2D, Concatenate, LeakyReLU, UpSampling2D
import keras.backend as K
import keras.utils.conv_utils as conv_utils
from tensorflow.keras.models import Model
import n... | 9,917 | 45.345794 | 116 | py |
LightDepth | LightDepth-main/tf_implementation/scripts/models/ordinary_unet.py | from tensorflow.keras.layers import Layer, InputSpec
import tensorflow as tf
from tensorflow.keras import applications
from tensorflow.keras.layers import Conv2D, Concatenate, LeakyReLU, UpSampling2D
import keras.backend as K
import keras.utils.conv_utils as conv_utils
from tensorflow.keras.models import Model
import n... | 9,771 | 45.312796 | 116 | py |
OPTMLSTM | OPTMLSTM-main/example_OPTM_LSTM.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Adam Ntakaris (adamantios.ntakaris@ed.ac.uk, @gmail.com)
"""
from keras.layers import Dense
import keras
import numpy as np
import OPTMCell
# Note: Random data example for illustration purposes only
# OPTM-LSTM is a narrow artificial intelligence model
# I... | 1,004 | 27.714286 | 75 | py |
OPTMLSTM | OPTMLSTM-main/OPTMCell.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Adam Ntakaris (adamantios.ntakaris@ed.ac.uk, @gmail.com)
Important: This is an extension based on
https://github.com/keras-team/keras/blob/v2.10.0/keras/layers/rnn/lstm.py
"""
import tensorflow.compat.v2 as tf
from keras import activations
from keras im... | 13,682 | 35.782258 | 79 | py |
InDuDoNet | InDuDoNet-main/test_clinic.py | import os.path
import os
import os.path
import argparse
import numpy as np
import torch
from CLINIC_metal.preprocess_clinic.preprocessing_clinic import clinic_input_data
from network.indudonet import InDuDoNet
import nibabel
import time
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
parser = argparse.ArgumentParser(descript... | 4,225 | 43.484211 | 116 | py |
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